Several methods have been implemented, familiar to one versed in the art, for integrating the information from multiple sensors to arrive at such a best estimate. For example, Kalman filtering is one technique that is used to iteratively derive the best estimate of a vehicle's position from different navigation sensors, while simultaneously determining the error components of each sensor. Such techniques share one precept: information is only incorporated in the solution from those sensors that have a detection, or “Hit” from the target. One familiar with the art of Signal Detection Theory (SDT) will recognize that a “Hit” is only one of four possible outcome permutations between a sensor and a target:
“HIT”: The sensor correctly detects the presence of a target;
“MISS”: The sensor fails to detect a target that is present;
“FALSE ALARM (FA)”: The sensor falsely detects a target where one does not exist;
“CORRECT REJECTION (CR)”: The sensor correctly determines that no target is present.
The signal detection theory provides statistical methods for addressing sensitivity thresholds that govern the balance between the “miss” and “false alarm” cases. By definition, there is no definitive way to distinguish between a “Hit” and a “false alarm” from a single sensor; if there were, the event would never be categorized as a “false alarm”. Similarly, there is no way to discriminate between a “Correct rejection” and a “miss”, from a single sensor, otherwise, there would never be any “miss” classifications.
There is a need for a method and apparatus that will overcome the above-identified drawbacks.